Article
Computer Science, Interdisciplinary Applications
Sy Nguyen-Van, Khoa T. Nguyen, Khanh D. Dang, Nga T. T. Nguyen, Seunghye Lee, Qui X. Lieu
Summary: In this study, an evolutionary symbiotic organisms search algorithm is developed for shape and size optimization of truss structures, combining differential evolution and symbiotic organisms search. By enhancing the exploration ability with a new symbiotic organisms search operator, as well as introducing a threshold and elitist scheme, the algorithm can achieve high-quality optimal solutions.
ADVANCES IN ENGINEERING SOFTWARE
(2021)
Article
Computer Science, Artificial Intelligence
Sanjoy Chakraborty, Sukanta Nama, Apu Kumar Saha
Summary: An improved SOS algorithm, named nwSOS, is proposed in this study to solve high-dimensional optimization problems by modifying benefit factors calculation and adjusting the parasitism phase. The algorithm successfully tackles multiple design issues and shows significant effectiveness in various aspects according to complexity, statistical, and convergence analysis.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Fahui Miao, Li Yao, Xiaojie Zhao
Summary: Sleep staging is crucial for preventing and diagnosing sleep disorders. However, the high dimensionality and abundance of redundant and irrelevant features in physiological signals pose challenges in studying sleep staging. This paper proposes an improved algorithm that enhances feature selection performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Construction & Building Technology
Mohammad H. Makiabadi, Mahmoud R. Maheri
Summary: The study developed an enhanced symbiotic organisms search algorithm which showed better efficiency in optimizing trusses with dynamic frequency constraints compared to other reported metaheuristic algorithms. By balancing exploitation and exploration capabilities, the ESOS algorithm proved to be generally more effective in solving optimization problems.
ADVANCES IN STRUCTURAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Hsing-Chih Tsai
Summary: This paper investigates and corrects the symbiotic organisms search (SOS) algorithm by proposing combination schemes to improve the performance of the corrected version in terms of early convergence speed, convergence precision, handling composition functions well, and achieving competitive performance on test problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Sy Nguyen-Van, Khoa T. Nguyen, Van Hai Luong, Seunghye Lee, Qui X. Lieu
Summary: This article proposes a novel optimization algorithm, named HDS, which combines DE and SOS for size and shape optimization of truss structures. The algorithm can enhance both global and local searching abilities effectively, achieve a better trade-off with an automatically adapted parameter, and utilize an elitist scheme for selecting the best solutions.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Jing Xiao, Yan-Jiao Wang, Xiao-Ke Xu
Summary: This study proposes a metaheuristic-based algorithm for fuzzy community detection, which improves global convergence by utilizing an improved bioinspired metaheuristic algorithm and speeds up convergence by utilizing neighbor-based membership modification. Experimental results show that the proposed algorithm outperforms other state-of-the-art algorithms in terms of accuracy and stability.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Fahui Miao, Li Yao, Xiaojie Zhao
Summary: CNN design usually requires manual iterations and adjustments, which are time-consuming and labor-intensive. Our proposed sosCNN algorithm automates the construction of strong CNN architectures.
APPLIED SOFT COMPUTING
(2021)
Article
Mathematics
Narayanan Ganesh, Rajendran Shankar, Kanak Kalita, Pradeep Jangir, Diego Oliva, Marco Perez-Cisneros
Summary: The effectiveness of a novel optimizer called MOSOS/D for multi-objective problems was investigated in this research. It was based on the symbiotic organisms' search and incorporated a decomposition framework for better performance. Both qualitative and quantitative analyses were conducted, showing the superiority of MOSOS/D in solving large complex multi-objective problems.
Article
Mathematics, Applied
Wenling Zhao, Ruyu Wang, Daojin Song
Summary: This paper proposes a class of smooth penalty functions for constrained optimization problem, which is based on LP and a smooth function of a class of exact penalty function l(P) (P ∈ (0,1]). A penalty algorithm is presented based on this class of penalty functions. Under a very weak condition, a perturbation theorem is established and the global convergence of the algorithm is derived. This result generalizes some existing conclusions. Finally, numerical experiments on two examples demonstrate the effectiveness and efficiency of the algorithm.
JOURNAL OF APPLIED MATHEMATICS AND COMPUTING
(2023)
Article
Computer Science, Information Systems
Bing-Chuan Wang, Han-Xiong Li, Yun Feng, Wen-Jing Shen
Summary: The paper proposes an adaptive fuzzy penalty method to address the issue of tuning the penalty coefficient in constrained evolutionary optimization, adjusting the coefficient at both individual and population levels. By using differential evolution to design a search algorithm, the constrained optimization evolutionary algorithm AFPDE is proposed, showing competitiveness through experiments.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Serhat Duman, Jie Li, Lei Wu, Nuran Yorukeren
Summary: In modern power systems, energy obtained from different generating units must be suitably planned for optimal operating conditions. This paper addressed the security-constrained AC-DC optimal power flow problem using a symbiotic organisms search (SOS) algorithm, taking into account the uncertainty of wind, solar, and plug-in electric vehicle (PEV) energy systems.
Article
Computer Science, Artificial Intelligence
Aljosa Vodopija, Joerg Stork, Thomas Bartz-Beielstein, Bogdan Filipic
Summary: This study addresses the challenge of optimizing elevator group control policies using constrained multiobjective optimization, employing true multiobjective optimization methods to find approximations for Pareto-optimal solutions. Experimental results demonstrate the scalability of the proposed methodology across various elevator systems and suggest that NSGA-II with the constrained-domination principle is the best performing algorithm for optimizing EGC.
APPLIED SOFT COMPUTING
(2022)
Article
Engineering, Chemical
Sh Dabagh, Y. Javid, F. M. Sobhani, A. Saghaiee, K. Parsa
Summary: This research proposes a multi-objective mathematical model for Self-Adaptive Risk-Based Inspection Planning (SARBIP) and validates the proposed model through a real case study in the Iranian petrochemical industry. The model aims to obtain optimal solutions by reducing both expenses and risk levels.
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
(2022)
Article
Chemistry, Analytical
Lisu Huo, Jianghan Zhu, Zhimeng Li, Manhao Ma
Summary: The study introduces a HDSOS algorithm that combines DE and SOS strategies, with both local and global search capabilities, as well as the introduction of traction function and perturbation strategy to enhance efficiency and robustness, comparative experiments demonstrate its superiority.
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)